
import pandas as pd
import math
import scipy
import numpy as np
import datetime 
import geopandas as gpd
import json
import itertools
from urllib.request import urlopen

%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
from plotly.offline import init_notebook_mode
import matplotlib.animation as animation

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"


with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)


# Read worldwide COVID data 
# https://ourworldindata.org/coronavirus/country/netherlands
covid_world = pd.read_csv('https://raw.githubusercontent.com/yangyi1141/TIL-LabAssignments-Group16/9bd1ff225fc7d0f9993e3c6481a75c34711641f1/owid-covid-data.csv')

# Daily Average Number of People Staying at Home
df = pd.read_csv('Trips_by_Distance.csv')

# Read data of passenger vehicles to US from Canada
# (https://www.bts.gov/covid-19/week-in-transportation)
df_ca = pd.read_csv('https://raw.githubusercontent.com/yangyi1141/TIL-LabAssignments-Group16/main/Passenger_Vehicles_to_US_from_Canada_data.csv')

# Read data of passenger vehicles to US from Mexico
# (https://www.bts.gov/covid-19/week-in-transportation)
df_me = pd.read_csv('https://raw.githubusercontent.com/yangyi1141/TIL-LabAssignments-Group16/main/Passenger_Vehicles_to_US_from_Mexico_data.csv')

# Get Percent Change in Passenger Travel from 2019 by Mode
df_passenger = pd.read_csv("https://raw.githubusercontent.com/yangyi1141/TIL-LabAssignments-Group16/main/Passenger_data.csv")

# Read data of the number of confirmed cases of COVID-19 in the USA
# (https://ourworldindata.org/explorers/coronavirus-data-explorer)
df_covid = pd.read_csv('https://raw.githubusercontent.com/yangyi1141/TIL-LabAssignments-Group16/main/owid-covid-data-2.csv')

# Read data of transit ridership of different transit modes
# (https://data.bts.gov/Research-and-Statistics/Monthly-Transportation-Statistics/crem-w557/data)
df_pt = pd.read_csv('https://raw.githubusercontent.com/yangyi1141/TIL-LabAssignments-Group16/main/Monthly_Transportation_Statistics.csv')

# Get Transportation Services Index and Seasonally-Adjusted Transportation Data
# (https://data.bts.gov/Research-and-Statistics/Transportation-Services-Index-and-Seasonally-Adjus/bw6n-ddqk/data)
df_service = pd.read_csv("https://raw.githubusercontent.com/yangyi1141/TIL-LabAssignments-Group16/main/Transportation_Services_Index_and_Seasonally-Adjusted_Transportation_Data(2).csv")

# Get Total flight depatures by week:2019,2020,2021,2022
df_depart = pd.read_csv("https://raw.githubusercontent.com/yangyi1141/TIL-LabAssignments-Group16/main/Total_Flight_Departures_by_Week_Total_Flights_data.csv")


# Extract the United States' COVID data 
covid_usa = covid_world[covid_world.location == 'United States']

# Reset index and drop unnecessary columns
covid_usa = covid_usa.reset_index()
covid_usa = covid_usa.drop(columns = ['index', 'iso_code','continent'])

# Convert the dates to datetime format
covid_usa_date = pd.to_datetime(covid_usa['date'], format='%Y-%m-%d')
covid_usa['date'] = covid_usa_date

# Fill NaN values with zero
covid_usa = covid_usa.fillna(0)
# covid_usa.head()


# Process the data so that the dates match with the dates in Part 4: Freight Transportation data
# Find the monthly mean covid cases 
covid_usa['month'] = covid_usa['date'].dt.month
covid_usa['year'] = covid_usa['date'].dt.year
covid_usa['day'] = 1
covid_usa['monthYear'] = pd.to_datetime(covid_usa[['month', 'year', 'day']])
mean_cases = covid_usa.groupby(['monthYear'])['new_cases_smoothed'].mean().to_frame(name = 'Cases').reset_index()
mean_cases.columns = ['DATE', 'Monthly mean new cases']

# Find the monthly mean covid deaths 
mean_deaths = covid_usa.groupby(['monthYear'])['new_deaths_smoothed'].mean().to_frame(name = 'Deaths').reset_index()
mean_deaths.columns = ['DATE', 'Monthly mean new deaths']

# Merge the monthly mean cases and deaths
cases_deaths = pd.merge(mean_cases, mean_deaths, on="DATE")


fig = px.line(covid_usa, x='date', y='new_cases_smoothed')

# Label the diagram
fig.update_xaxes(title_text='Date', showgrid = False)
fig.update_yaxes(title_text='Cases (persons)')
fig.update_layout(title_text = 'Daily New COVID-19 Cases in the United States (2020-2022)')
fig.update_traces(line_color='blue', line_width=3)

# Store as a json file
fig.write_json('q1_1.json')

# Show figure
fig.show()


fig = px.line(covid_usa,x='date', y='new_deaths_smoothed')

# Label the diagram
fig.update_xaxes(title_text = 'Date', showgrid=False)
fig.update_yaxes(title_text = 'Deaths (persons)')
fig.update_layout(title_text = 'Daily New COVID-19 Deaths in the United States (2020-2022)')
fig.update_traces(line_color='red', line_width=3)

# Store as a json file
fig.write_json('q1_2.json')

# Show figure
fig.show()


# Data processing
# Transfer to pandas time 
df.loc[:, 'Date'] = pd.to_datetime(df.loc[:, 'Date'])
df['Year'] = df['Date'].dt.year 

# Calculate percentage of people staying at home
df['Percentage Staying'] = 100 * df['Population Staying at Home'] / (df['Population Staying at Home'] + df['Population Not Staying at Home'])

# Get national data in 2019, 2020, 2021
df_n = df.groupby('Level').get_group('National')
n19 = df_n.groupby('Year').get_group(2019)
n20 = df_n.groupby('Year').get_group(2020)
n21 = df_n.groupby('Year').get_group(2021)

# Get weekly mean value
perc19 = n19.groupby('Week')['Percentage Staying'].mean()
perc20 = n20.groupby('Week')['Percentage Staying'].mean()
perc21 = n21.groupby('Week')['Percentage Staying'].mean()

# Create a new dataframe for weekly mean value
perc = pd.DataFrame(data={
    '2019':perc19,
    '2020':perc20,
    '2021':perc21}
)


# Get county data
df_c = df.groupby('Level').get_group('County')
# Select the data of April 2020
c20 = df_c.groupby('Year').get_group(2020)
c21 = df_c.groupby('Year').get_group(2021)
apr20 = c20.groupby('Month').get_group(4)
nov21 = c21.groupby('Month').get_group(11)
mean1 = apr20.groupby('County FIPS')['Percentage Staying'].mean()
dict1 = {'County FIPS': mean1.index,'Percentage Staying': mean1.values}
map1 = pd.DataFrame(dict1)

# Select the data of November 2021
mean2 = nov21.groupby('County FIPS')['Percentage Staying'].mean()
dict2 = {'County FIPS':mean2.index,'Percentage Staying':mean2.values}
map2 = pd.DataFrame(dict2)


# Line chart
fig = px.line(perc,markers = True)
fig.update_xaxes(title_text="Week during a year")
fig.update_yaxes(title_text="Percentage of People Staying at Home")
fig.update_layout(title_text="Weekly Average Pencentage of People Staying at Home: 2019–2021")

# Store as a json file
fig.write_json('q2_1.json')
fig.show()


# Heat map for Percentage Staying at Home April 2020
fig = go.Figure(data=go.Choropleth(
    locations = map1['County FIPS'],
    geojson=counties,
    z = map1['Percentage Staying'],
    zmin=0,
    zmax=80,
    colorscale = 'Viridis',
    autocolorscale=False,
    marker_line_color='darkgray',
    marker_line_width=0.5,
    colorbar_title = 'Percentage'
))

fig.update_layout(
    title_text='Percentage Staying at Home April 2020',
    geo_scope='usa', # limite map scope to USA
    geo=dict(
        showframe=False,
        showcoastlines=False
    )
)

# Store as a json file
fig.write_json('q2_2.json')

fig.show()


# Heat map for Percentage Staying at Home November 2021
fig = go.Figure(data=go.Choropleth(
    locations = map2['County FIPS'],
    geojson=counties,
    z = map2['Percentage Staying'],
    zmin=0,
    zmax=80,
    colorscale = 'Viridis',
    autocolorscale=False,
    marker_line_color='darkgray',
    marker_line_width=0.5,
    colorbar_title = 'Percentage'
))

fig.update_layout(
    title_text='Percentage Staying at Home November 2021',
    geo_scope='usa', # limite map scope to USA
    geo=dict(
        showframe=False,
        showcoastlines=False
    )
)

# Store as a json file
fig.write_json('q2_3.json')

fig.show()


# Find the number of trips on the 1st of the month
df1 = df.iloc[[365,396,425,456,486,517,547,578,609,639,670,700]]
# The figure of number of trips in 2020
fig = go.Figure()
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["Number of Trips <1"],mode='lines+markers',name='Number of Trips <1'))
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["Number of Trips 1-3"],mode='lines+markers',name='Number of Trips 1-3'))
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["Number of Trips 3-5"],mode='lines+markers',name='Number of Trips 3-5'))
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["Number of Trips 5-10"],mode='lines+markers',name='Number of Trips 5-10'))
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["Number of Trips 10-25"],mode='lines+markers',name='Number of Trips 10-25'))
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["Number of Trips 25-50"],mode='lines+markers',name='Number of Trips 25-50'))
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["Number of Trips 50-100"],mode='lines+markers',name='Number of Trips 50-100'))
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["Number of Trips 100-250"],mode='lines+markers',name='Number of Trips 100-250'))
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["Number of Trips 250-500"],mode='lines+markers',name='Number of Trips 250-500'))
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["Number of Trips >=500"],mode='lines+markers',name='Number of Trips >=500'))
fig.update_xaxes(title_text="date")
fig.update_yaxes(title_text="number of trips")
fig.update_layout(
    title="The change in number of trips in 2020",
    autosize=False,
    width=1000,
    height=600,
)

# Store as a json file
fig.write_json('q3_1.json')

fig.show()


# Find the number of trips on the 1st of the month
df2 = df.iloc[[0,31,59,90,120,151,181,212,243,273,303,334]]
# The figure of number of trips in 2019
fig = go.Figure()
fig.add_trace(go.Scatter(x=df2["Date"], y=df2["Number of Trips <1"],mode='lines+markers',name='Number of Trips <1'))
fig.add_trace(go.Scatter(x=df2["Date"], y=df2["Number of Trips 1-3"],mode='lines+markers',name='Number of Trips 1-3'))
fig.add_trace(go.Scatter(x=df2["Date"], y=df2["Number of Trips 3-5"],mode='lines+markers',name='Number of Trips 3-5'))
fig.add_trace(go.Scatter(x=df2["Date"], y=df2["Number of Trips 5-10"],mode='lines+markers',name='Number of Trips 5-10'))
fig.add_trace(go.Scatter(x=df2["Date"], y=df2["Number of Trips 10-25"],mode='lines+markers',name='Number of Trips 10-25'))
fig.add_trace(go.Scatter(x=df2["Date"], y=df2["Number of Trips 25-50"],mode='lines+markers',name='Number of Trips 25-50'))
fig.add_trace(go.Scatter(x=df2["Date"], y=df2["Number of Trips 50-100"],mode='lines+markers',name='Number of Trips 50-100'))
fig.add_trace(go.Scatter(x=df2["Date"], y=df2["Number of Trips 100-250"],mode='lines+markers',name='Number of Trips 100-250'))
fig.add_trace(go.Scatter(x=df2["Date"], y=df2["Number of Trips 250-500"],mode='lines+markers',name='Number of Trips 250-500'))
fig.add_trace(go.Scatter(x=df2["Date"], y=df2["Number of Trips >=500"],mode='lines+markers',name='Number of Trips >=500'))
fig.update_xaxes(title_text="date")
fig.update_yaxes(title_text="number of trips")
fig.update_layout(
    title="The change in number of trips in 2019",
    autosize=False,
    width=1000,
    height=600,
)

# Store as a json file
fig.write_json('q3_2.json')

fig.show()


# Find the number of trips on the 1st of the month
df3 = df.iloc[[731,762,790,821,851,882]]
# The figure of number of trips in 2021
fig = go.Figure()
fig.add_trace(go.Scatter(x=df3["Date"], y=df3["Number of Trips <1"],mode='lines+markers',name='Number of Trips <1'))
fig.add_trace(go.Scatter(x=df3["Date"], y=df3["Number of Trips 1-3"],mode='lines+markers',name='Number of Trips 1-3'))
fig.add_trace(go.Scatter(x=df3["Date"], y=df3["Number of Trips 3-5"],mode='lines+markers',name='Number of Trips 3-5'))
fig.add_trace(go.Scatter(x=df3["Date"], y=df3["Number of Trips 5-10"],mode='lines+markers',name='Number of Trips 5-10'))
fig.add_trace(go.Scatter(x=df3["Date"], y=df3["Number of Trips 10-25"],mode='lines+markers',name='Number of Trips 10-25'))
fig.add_trace(go.Scatter(x=df3["Date"], y=df3["Number of Trips 25-50"],mode='lines+markers',name='Number of Trips 25-50'))
fig.add_trace(go.Scatter(x=df3["Date"], y=df3["Number of Trips 50-100"],mode='lines+markers',name='Number of Trips 50-100'))
fig.add_trace(go.Scatter(x=df3["Date"], y=df3["Number of Trips 100-250"],mode='lines+markers',name='Number of Trips 100-250'))
fig.add_trace(go.Scatter(x=df3["Date"], y=df3["Number of Trips 250-500"],mode='lines+markers',name='Number of Trips 250-500'))
fig.add_trace(go.Scatter(x=df3["Date"], y=df3["Number of Trips >=500"],mode='lines+markers',name='Number of Trips >=500'))
fig.update_xaxes(title_text="date")
fig.update_yaxes(title_text="number of trips")
fig.update_layout(
    title="The change in number of trips in 2021",
    autosize=False,
    width=1000,
    height=600,
)

# Store as a json file
fig.write_json('q3_3.json')

fig.show()


# Change column type to pandas date time
df_ca.loc[:, 'Date'] = pd.to_datetime(df_ca.loc[:,'Date'])
df_ca = df_ca.set_index('Date')

# Define a figure
fig = px.line(
    df_ca,
    x=df_ca.index,
    y=df_ca.columns
)
# Labeling the axes of the diagram
fig.update_xaxes(title_text='Time')
fig.update_yaxes(title_text='Passenger Vehicles')
# Adding title to the diagram
fig.update_layout(
    title_text='Comparison of Passenger Vehicles to US from Canada before and during COVID-19'
)

# Store as a json file
fig.write_json('q4_1.json')

# Show the figure
fig.show()


# Change column type to pandas date time
df_me.loc[:, 'Date'] = pd.to_datetime(df_me.loc[:,'Date'])
df_me = df_me.set_index('Date')

# Define a figure
fig = px.line(
    df_me,
    x=df_me.index,
    y=df_me.columns
)
# Labeling the axes of the diagram
fig.update_xaxes(title_text='Time')
fig.update_yaxes(title_text='Passenger Vehicles')
# Adding title to the diagram
fig.update_layout(
    title_text='Comparison of Passenger Vehicles to US from Mexico before and during COVID-19'
)

# Store as a json file
fig.write_json('q4_2.json')

# Show the figure
fig.show()


# Change column type to pandas date time of Percent Change in Passenger Travel from 2019 by Mode
df_passenger.loc[:, "Day(Week Ending)"] = pd.to_datetime(df_passenger.loc[:, "Day(Week Ending)"])
df_passenger

fig = px.line(
    df_passenger,
    x=df_passenger.loc[:, "Day(Week Ending)"],
    y=df_passenger.loc[:, "Week Change"],
    color="Mode"
)

# Labeling the axes of a diagram
fig.update_xaxes(
    title_text="Time",
    showgrid=False  # No grid lines are drawn for the x-axis
)
fig.update_yaxes(title_text="Change from baseline")
fig.update_layout(
    title_text="Percent Change in Passenger Travel from 2019 by Mode"  #Adding title to the chart
)


# Store as a json file
fig.write_json('q5.json')

# Show figure
fig.show()


# Process ata of confirmed cases in the USA
# Extract valid data of confirmed cases in the USA for the first day of each month
df_covid = df_covid[df_covid['location']=='United States']
df_covid = df_covid.loc[217666:218547,['date','new_cases']]
df_covid.index = range(len(df_covid))
df_covid = df_covid.iloc[[0,29,60,90,121,151,182,213,243,274,304,335,366,394,425,455,
                          486,516,547,578,608,639,669,700,731,759,790,820,851,881],:]
# Redefine the order of index
df_covid.index = range(len(df_covid))
# Expand the value of the number of confirmed cases to a value range similar to that of riders
df_covid['new_cases'] = df_covid['new_cases'] * 1000

# Process data of riders for different public transits in the USA
df_pt.loc[:, 'Date'] = pd.to_datetime(df_pt.loc[:,'Date'])
df_pt = df_pt.loc[877:906,['Date','Transit Ridership - Other Transit Modes - Adjusted',
                          'Transit Ridership - Fixed Route Bus - Adjusted','Transit Ridership - Urban Rail - Adjusted']]
df_pt.index = range(len(df_pt))

# Combine the dataframes
df_com = pd.concat([df_covid,df_pt], axis=1)
df_com = df_com.drop('Date', axis=1)
df_com = df_com.set_index('date')


# Define a figure
fig = px.line(
    df_com,
    x=df_com.index,
    y=df_com.columns
)
# Labeling the axes of the diagram
fig.update_xaxes(title_text='Time')
fig.update_yaxes(title_text='Number of people')
# Adding title to the diagram
fig.update_layout(
    title_text='Relationship Between Confirmed Cases and Different Transit Modes'  
)

# Store as a json file
fig.write_json('q6.json')

# Show the figure
fig.show()


# Rename columns
df_service = df_service.rename({'OBS_DATE': 'DATE'}, axis=1)
# Change column type to pandas date time of Percent Change in Passenger Travel from 2019 by Mode
df_service.loc[:, "DATE"] = pd.to_datetime(df_service.loc[:, "DATE"])


# Remove the Weekly Total Flights item that duplicates every year
df_depart_new = df_depart.drop_duplicates(subset=["date (International) Week","date (International) Year"])


fig = go.Figure(data=[
    # Unadjusted passenger transportation data series
    go.Scatter(name="Unadjuested", x=df_service.loc[:, "DATE"], y=df_service.loc[:, "RPM"]),
    # Seasonally-adjusted passenger transportation data series
    go.Scatter(name="Seasonally-adjusted", x=df_service.loc[:, "DATE"], y=df_service.loc[:, "RPM_D11"])
])

# Labeling the axes of a diagram
fig.update_xaxes(title_text="Year")
fig.update_yaxes(title_text="Air Revenue Passenger Miles(RPMs)")

# Show vertical and horizontal line (crosshair) as hoverinfo
fig.update_xaxes(spikemode="across")
fig.update_yaxes(spikemode="across")


fig.update_layout(
    title="Monthly Changes in Air Travel for Equivalent Periods",  #Adding title to the chart
    hovermode="x unified"  # Make hover messages display two data points
)

# Store as a json file
fig.write_json('q7_1.json')

# Show figure
fig.show()


fig = px.line(
    df_depart_new,
    x=df_depart_new.loc[:, "date (International) Week"],
    y=df_depart_new.loc[:, "Weekly Total Flights"],
    color="date (International) Year"
)

# Labeling the axes of a diagram
fig.update_xaxes(
    title_text="Month",
    showgrid=False  # No grid lines are drawn for the x-axis
)
fig.update_yaxes(title_text="Total Flights Departures by Week: 2019,2020,2021,2022")

fig.update_layout(
    title_text="Airline Travel as Measured by Airport Screenings as a Proxy: 2019–2022",  #Adding title to the chart
    hovermode = "x" ,          # Make hover messages display 4 years of data at the same time
    xaxis = dict(tickmode='array',
                 tickvals=df_depart_new.loc[:, "date (International) Week"],  # Original value
                 ticktext=['Jan','','','','Feb','','','','Mar','','','',
                           'Apr','','','','May','','','','','Jun','','','',
                           'Jul','','','','','Aug','','','','Sep','','','',
                           'Oct','','','','','Nov','','','','Dec','','',''],  # Final displayed value (replaced value)
                 tickangle=0   # Make ticks horizontal for easy reading
    ) 
)

# Store as a json file
fig.write_json('q7_2.json')

# Show figure
fig.show()


# Read total transportation data of the United States
# https://data.bts.gov/Research-and-Statistics/Transportation-Services-Index-and-Seasonally-Adjus/bw6n-ddqk
transport_usa = df_service
# Filter for days that match the COVID data of the United States
transport_usa = transport_usa[(transport_usa['DATE'] > '2019-12-01') & (transport_usa['DATE'] < '2022-11-01')]


# Extract freight data by dropping irrelevant columns
freight_usa = transport_usa.drop(columns = [
    'ID', 'ASM', 'ASM_D11', 'ASM_D', 'ASM_D_D11', 'ASM_I', 'ASM_I_D11', 'RPM', 'RPM_D11', 'RPM_D', 'RPM_D_D11', 
    'RPM_I','RPM_I_D11', 'LOAD_FACTOR', 'LOAD_FACTOR_D11', 'LOAD_FACTOR_D','LOAD_FACTOR_D_D11', 
    'LOAD_FACTOR_I', 'LOAD_FACTOR_I_D11','ENPLANE_I', 'ENPLANE_I_D11', 'ENPLANE_D', 'ENPLANE_D_D11', 
    'ENPLANE', 'ENPLANE_D11','AIR_RPM_TSI', 'AIR_RPM_TSI_D11', 'RAIL_PM', 'RAIL_PM_D11', 'TRANSIT',
    'TRANSIT_D11', 'PETROLEUM', 'PETROLEUM_D11', 'NATURAL_GAS', 'NATURAL_GAS_D11', 'VMT','VMT_D11',
    'TSI_Passenger', 'TSI_Passenger_C', 'TSI_Freight', 'TSI_Freight_C', 'TSI_Total', 'TSI_Total_C', 
    'IND_PRO', 'MANUF','IDX_AIR_RPM', 'IDX_AIR_RTMFM', 'IDX_RAIL_FRT_CARLOADS','IDX_RAIL_FRT_INTERMODAL', 
    'IDX_RAIL_PM', 'IDX_TRANSIT_D11', 'IDX_PETROLEUM_D11', 'IDX_NATURAL_GAS_D11', 'IDX_WATERBORNE_D11',
    'IDX_TRUCK_D11', 'AIR_RTMFM', 'RAIL_FRT_CARLOADS', 'RAIL_FRT_INTERMODAL', 'WATERBORNE', 'INV_TO_SALES'
])

# Rename column names
freight_usa.columns = ['DATE', 'Air Freight (Ton Miles)', 'Rail Freight', 'Rail Intermodal', 'Waterborne (Million Tons)', 'Truck Tonnage Index']

# Fill NaN values with zeros
#freight_usa = freight_usa.fillna(0)

# Rename columns
freight_usa = freight_usa.rename({'OBS_DATE': 'DATE'}, axis=1)

# Convert data type to float
freight_usa = freight_usa.replace(',','', regex=True)
freight_usa.iloc[:, 1:7] = freight_usa.iloc[:, 1:7].astype(float)
# freight_usa.head()


# Process the freight data for ease of visualisation later
freight_usaNew = freight_usa.iloc[:, 0:6].melt(id_vars=["DATE"], 
        var_name="Type", 
        value_name="Number")

# Merge the processed covid data and the freight data
covid_freight = pd.merge(cases_deaths, freight_usa, on="DATE")

# Find correlation matrix within the merged data
matrix = covid_freight.corr(
    method = 'pearson',  # The method of correlation
    min_periods = 1      # Min number of observations required
)

# Store the merged data
covid_freight.to_csv('covid_freight.csv', encoding='utf-8', index=False)


# Plot the different freight transport data based on dates in a line plot
# Differentiate the lines by the freight type
fig = px.line(
    freight_usaNew, x="DATE", y="Number", color="Type", 
    hover_data=['Number']
)

# Labeling the axes of a diagram
fig.update_xaxes(title_text='Date',showgrid=False)
fig.update_yaxes(title_text='Load')
fig.update_layout(title_text='Monthly Freight Transportation in the United States (2020-2022)')
fig.update_traces(line_width=3)

# Store as a json file
fig.write_json('q8.json')

fig.show()


plt.figure(figsize = (16,5))
plt.title('Correlation for COVID-19 and Freight Transportation in the United States',fontsize = 20)
matrix_new = covid_freight.corr().round(2)
sns.heatmap(matrix, annot=True, vmax=1, vmin=-1, center=0, cmap='vlag')
plt.show()

Project Group - 16¶

Research Objective¶

Contribution Statement¶

Import libraries¶

Data Import¶

Question 1: How has the number of new cases/deaths of COVID-19 changed?¶

New COVID-19 cases and deaths in the United States on a daily basis¶

Question 2: What are the changes in the number of people staying at home before and during COVID-19?¶

Question 3: Have people made more or less personal trips before and during COVID-19?¶

Question 4: What is the difference in passenger vehicles between the north and the south during COVID-19?¶

Question 5: How has passenger travel changed in mode before and during COVID-19?¶

Question 6: How has public transportation been specifically affected by COVID-19?¶

Question 7: How has air travel for passengers changed before and during COVID-19?¶

Question 8: How has the number of freight transportation trips changed before and during COVID-19?¶

Description of the extracted dataset of freight transportation¶

Fluctuations in loads for different freight transportation¶

Correlation between COVID-19 and freight transportation data¶